Bibliographic record
Abstract
Drop\nimpact on a heated surface not only displays intriguing flow\nmotion but also plays a crucial role in various applications and processes.\nWe examine the impact dynamics of a water drop on both heated flat\nand nanostructured surfaces, with a wide range of impact velocity\n(<i>V</i>) and surface temperature (<i>T</i><sub>s</sub>) values. Via high-speed imaging and temperature measurements,\nwe construct phase diagrams of different impact outcomes on these\nheated surfaces. Like those on the heated flat surface, water drops\ncan deposit, spread, rebound, or break-up with atomizing on the heated\nnanostructures as <i>V</i> and <i>T</i><sub>s</sub> are increased. We find a significant influence of nanostructures\non the impact dynamics by generating particular events in specific\nparameter ranges. For example, events of splashing, gentle central\njetting, and violent central jetting are observed on and thus triggered\nby the heated nanostructures. The heated nanotextures with high roughness\ncan easily trigger the splashing and the central jetting. Our data\nof the normalized maximum spreading diameter for the heated surfaces\ndisplay distinct trends at low and high Weber number (<i>We</i>) ranges, where <i>We</i> compares the kinetic to surface\nenergy of the impacting droplet. Finally, compared with the flat surface,\nthe dynamic Leidenfrost temperature (<i>T</i><sub>L</sub><sup>D</sup>) for <i>We</i> ≈ 10 is decreased (by ≈60 °C) by the high-roughness\nnanotextures. In addition, our experimental data of <i>T</i><sub>L</sub><sup>D</sup> is consistent\nwith a model prediction proposed by balancing the droplet dynamic\nand vapor pressure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.204 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".